The field of energy and resource allocation is witnessing a significant shift towards developing more efficient and fair mechanisms. Researchers are exploring new frameworks and algorithms that can allocate resources in a way that maximizes social welfare and minimizes inequality. One of the key directions is the development of online fair allocation mechanisms that can handle sequential arrivals of goods or services and allocate them to agents in a fair and efficient manner. Another important area of research is the design of budget-feasible mechanisms that can procure goods or services from strategic agents while respecting budget constraints. The use of predictions and machine learning techniques is also being investigated to improve the performance of these mechanisms. Noteworthy papers in this area include: A Causation-Based Framework for Pricing and Cost Allocation of Energy, Reserves, and Transmission in Modern Power Systems, which proposes a novel pricing framework for electricity markets. Online Fair Division with Additional Information, which demonstrates how additional information can be used to design fairer online algorithms. An O(log log n)-approximate budget feasible mechanism for subadditive valuations, which improves the state-of-the-art in budget-feasible mechanism design for subadditive valuations.